深度学习在无处不在的人机交互的6G时代:应用,原则和前景.
Chunlei Chen1, Huixiang Zhang2, Jinkui Hou1
1School of Computer Engineering, Weifang University, Weifang 261061, China.
Biomimetics (Basel, Switzerland)
|August 25, 2023
概括
深度学习为6G系统的六个关键挑战提供了解决方案,使以人为中心的智能成为可能. 这篇评论探讨了深度学习.
科学领域:
- 电信工程 电信工程 电信工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 虚拟现实 (VR) 和增强现实 (AR) 的出现预示着无处不在的人为中心智能的新时代.
- 第六代 (6G) 无线系统对于未来的无人机互动至关重要.
- 6G承诺与前几代相比显著提高性能,使先进的应用程序成为可能.
研究的目的:
- 解决阻碍6G系统发展的挑战.
- 探索深度学习技术的应用,以克服这些6G挑战.
- 为6G提供深度学习解决方案的系统审查.
主要方法:
- 审查六个关键6G挑战的代表性深度学习解决方案:特拉赫兹/毫米波通信,低延迟/高可靠性,能源效率,安全性,边缘计算和服务异质性.
- 分析将深度学习应用到特定6G问题上的原则.
- 调查深度强化学习的作用和解决6G中的数据稀缺问题.
主要成果:
- 深度学习为复杂的6G问题提供了传统分析方法的有效替代方案.
- 深度强化学习被认为对6G系统至关重要.
- 介绍了用于训练数据稀缺性的解决方案,以及对传统方法和6G中的深度学习之间的协同作用的见解.
- 确定了用于6G的常用深度学习技术.
结论:
- 深度学习对于实现6G以人为中心的应用程序的全部潜力至关重要.
- 通过人工智能解决通信,效率,安全和计算方面的挑战对6G至关重要.
- 未来的研究应该专注于6G网络中的开放问题和先进的深度学习应用.
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